A method for treating soil composite heavy metal pollution in non-ferrous smelting sites

Abstract

The invention discloses a method for treating composite heavy metal pollution in soil of non-ferrous smelting sites, and relates to the field of pollution treatment. The method comprises the following steps: obtaining soil conditions of the site, analyzing the soil conditions of the site to obtain soil layout, clay mineral conditions and heavy metal distribution conditions of the site; conducting heavy metal adsorption experiments on clay minerals to obtain adsorption data; constructing a retention prediction model in combination with the soil layout of the site, clay minerals, heavy metal distribution conditions and adsorption data; obtaining a retention prediction value according to the retention prediction model, comparing the retention prediction value with the current heavy metal distribution conditions to determine the types of clay minerals in the passivated excessive heavy metals and their distribution; determining the effective content of heavy metals according to a risk index, and generating a strategy for treating composite heavy metal pollution in soil of non-ferrous smelting sites in combination with the heavy metal distribution conditions. The invention quantifies the amount of clay minerals used in combination with the heavy metal pollution level of the site, and solves the problem of poor treatment effect on composite heavy metal pollution sites.

Description

Technical Field

[0001] The invention relates to the field of pollution treatment, in particular to a method for treating soil composite heavy metal pollution in non-ferrous smelting sites. Background Art

[0002] With the continuous upgrading of the nonferrous metal industry, large areas of abandoned industrial sites have appeared in resource-based cities and their surrounding areas. These sites often have serious heavy metal pollution problems, posing a great threat to the ecological environment and public health.

[0003] Due to the complex hydrogeology, wide variety of metal elements and large differences in pollution levels of non-ferrous smelting sites, existing technologies have many shortcomings in dealing with multi-metal pollution, stable and lasting effectiveness, ecological degradation risk prevention and control, and the degree of technical coordination.

[0004] In order to solve these problems, a method for treating the complex heavy metal pollution in the soil of non-ferrous smelting sites is urgently needed. Summary of the invention

[0005] To solve the above problems, the present application proposes a method for treating soil composite heavy metal pollution in non-ferrous smelting sites, comprising the following steps:

[0006] S1. Obtain the soil conditions of the site, analyze the soil conditions of the site to obtain the soil layout, clay mineral conditions and heavy metal distribution of the site;

[0007] S2. Conduct heavy metal adsorption experiments on clay minerals to obtain adsorption data;

[0008] S3, construct a retention prediction model based on site soil layout, clay minerals, heavy metal distribution and adsorption data;

[0009] S4. Obtain a predicted immobilization value according to the immobilization prediction model, and compare the predicted immobilization value with the current heavy metal distribution to determine the types of clay minerals and their distribution in the passivated excessive heavy metals;

[0010] S5. Determine the effective content of heavy metals based on the risk index, and generate a strategy for controlling complex heavy metal pollution in the soil of non-ferrous smelting sites based on the distribution of heavy metals.

[0011] Preferably, the specific contents of constructing the fixation prediction model in combination with the site soil layout, heavy metal distribution and adsorption data include:

[0012] Generate a virtual model of the site based on the soil layout ratio of the site, and divide the virtual model into blocks according to the distribution of heavy metals to obtain heavy metal blocks;

[0013] Analyze the clay minerals to obtain the types and distribution of clay minerals;

[0014] Generate corresponding identifiers according to the types of clay minerals, and mark and display the identifiers in the heavy metal blocks according to the distribution of clay minerals;

[0015] The heavy metal range is rendered with the identifier as the center to obtain a rendering block, and the depth of the rendering color is determined according to the heavy metal concentration;

[0016] Calculate the area of ​​the rendering blocks and sort them from small to large to get the area order.

[0017] Preferably, it is characterized in that the specific contents of constructing the fixation prediction model in combination with the site soil layout, heavy metal distribution and adsorption data also include:

[0018] Acquire adsorption data, establish a mineral adsorption curve with time as the x-axis and adsorption degree as the y-axis, and establish a display window on the adsorption curve;

[0019] The identifier and its rendering area are intercepted, and the corresponding mineral adsorption curve is matched according to the identifier, and the corresponding display window is matched according to the heavy metal concentration;

[0020] According to the corresponding situation of the y-axis of the adsorption curve, rendering prediction is performed in the remaining display window of the corresponding mineral adsorption curve;

[0021] The predicted display windows are merged in chronological order to generate a dynamic retention prediction model.

[0022] Preferably, the specific contents of obtaining adsorption data and establishing a mineral adsorption curve with time as the x-axis and adsorption degree as the y-axis are as follows:

[0023] Preprocess the adsorption data to remove abnormal values ​​and obtain sample data;

[0024] A plane rectangular coordinate system is established with time as the x-axis and adsorption degree as the y-axis;

[0025] Fill the sample data into the rectangular coordinate system in the form of scattered points;

[0026] A standard kinetic equation is preset, and the sample data is substituted into the equation for matching and screening. The equation with the smallest difference value is the target kinetic equation;

[0027] According to natural factors and environmental factors, the fluctuation factors are configured for the target kinetic equation to obtain the mineral adsorption curve equation;

[0028] The mineral adsorption curve is established according to the mineral adsorption curve equation.

[0029] Preferably, the retention prediction value is obtained according to the retention prediction model, and the retention prediction value is compared with the current heavy metal distribution to determine the types of clay minerals in the passivated excessive heavy metals and their distribution.

[0030] The clay mineral with the first area order is selected as the target mineral;

[0031] The rendering blocks with non-zero predicted retention values ​​are selected as target mineral distribution blocks.

[0032] Preferably, the effective content of heavy metals is determined according to the risk index, and the specific content of the strategy for treating the composite heavy metal pollution in the soil of non-ferrous smelting sites is generated in combination with the distribution of heavy metals:

[0033] A risk index is preset according to different social and environmental conditions, and the current effective content of target heavy metals in the soil is calculated based on the risk index;

[0034] Get the current heavy metal content based on the heavy metal distribution;

[0035] The predicted immobilization values ​​were analyzed to obtain the predicted adsorbed heavy metal content;

[0036] Calculate the target adsorbed heavy metal content as the difference between the current heavy metal content and the predicted adsorbed heavy metal content and the target heavy metal effective state content;

[0037] The total target mineral demand is obtained based on the target heavy metal adsorption content and the immobilization prediction model;

[0038] A genetic algorithm is used to calculate the target mineral block demand in the target mineral distribution block to generate a strategy for controlling complex heavy metal pollution in the soil of non-ferrous smelting sites.

[0039] Preferably, the specific content of the strategy for treating the soil composite heavy metal pollution in non-ferrous smelting sites by using the genetic algorithm to calculate the target mineral block demand of the target mineral distribution block is as follows:

[0040] Determine the accuracy of the current genetic algorithm according to environmental requirements, and divide the total demand for target minerals into demand monomers according to the accuracy of the genetic algorithm;

[0041] Match the demand monomers into the target mineral blocks and make retention predictions in the retention prediction model;

[0042] Calculate the predicted area of ​​different blocks until the predicted area of ​​different blocks is 0 and stop the iteration. Record the current number of monomers required in different blocks, and generate the target mineral block demand based on the current number of monomers required in different blocks to generate a strategy for controlling complex heavy metal pollution in the soil of non-ferrous smelting sites.

[0043] In summary, compared with traditional technologies, the method for treating composite heavy metal pollution in the soil of non-ferrous smelting sites of the present invention constructs a retention prediction model based on the clay mineral content data in the composite heavy metal contaminated soil of the site in combination with indoor adsorption experiments, and quantifies the amount of clay minerals used in combination with the heavy metal pollution level of the site, thereby solving the problem of poor treatment effect on composite heavy metal contaminated sites.

[0044] The technical method of the present invention is further described in detail below through the drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a step diagram of a method for treating composite heavy metal pollution in soil of non-ferrous smelting sites according to the present invention. DETAILED DESCRIPTION

[0046] The technical method of the present invention is further described below by means of the accompanying drawings and embodiments. It should be noted that unless otherwise specifically stated, the relative arrangement of the components and steps, numerical expressions and numerical values ​​described in these embodiments do not limit the scope of the present application.

[0047] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present application, its application, or uses.

[0048] Technologies, systems, and devices known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, systems, and devices should be considered part of the specification.

[0049] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0050] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0051] The main purpose of the present invention is to solve some defects existing in the existing image compression technology, especially the problem that when compressing images in machine vision applications, the relationship between the image volume after compression and the retention of effective visual data information of the image is not fully considered, and then compressing the image according to a fixed compression ratio leads to the loss of effective visual information after image compression.

[0052] The present invention provides a method for treating soil composite heavy metal pollution in non-ferrous smelting sites, comprising the following steps:

[0053] S1. Obtain the soil conditions of the site, analyze the soil conditions of the site to obtain the soil layout, clay mineral conditions and heavy metal distribution of the site;

[0054] The soil conditions include soil distribution and soil physical and chemical properties. The soil physical and chemical properties include pH value (PH), organic matter content (SOM), clay content (Clay), cation exchange capacity (CEC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), soil bulk density, etc.

[0055] S2. Conduct heavy metal adsorption experiments on clay minerals to obtain adsorption data;

[0056] S3, construct a retention prediction model based on site soil layout, clay minerals, heavy metal distribution and adsorption data;

[0057] Preferably, the specific contents of constructing the fixation prediction model in combination with the site soil layout, heavy metal distribution and adsorption data include:

[0058] Generate a virtual model of the site based on the soil layout ratio of the site, and divide the virtual model into blocks according to the distribution of heavy metals to obtain heavy metal blocks;

[0059] Analyze the clay minerals to obtain the types and distribution of clay minerals;

[0060] Generate corresponding identifiers according to the types of clay minerals, and mark and display the identifiers in the heavy metal blocks according to the distribution of clay minerals;

[0061] The heavy metal range is rendered with the identifier as the center to obtain a rendering block, and the depth of the rendering color is determined according to the heavy metal concentration;

[0062] Calculate the area of ​​the rendering blocks and sort them from small to large to get the area order.

[0063] Preferably, it is characterized in that the specific contents of constructing the fixation prediction model in combination with the site soil layout, heavy metal distribution and adsorption data also include:

[0064] Acquire adsorption data, establish a mineral adsorption curve with time as the x-axis and adsorption degree as the y-axis, and establish a display window on the adsorption curve;

[0065] The identifier and its rendering area are intercepted, and the corresponding mineral adsorption curve is matched according to the identifier, and the corresponding display window is matched according to the heavy metal concentration;

[0066] According to the corresponding situation of the y-axis of the adsorption curve, rendering prediction is performed in the remaining display window of the corresponding mineral adsorption curve;

[0067] The predicted display windows are merged in chronological order to generate a dynamic retention prediction model.

[0068] Preferably, the specific contents of obtaining adsorption data and establishing a mineral adsorption curve with time as the x-axis and adsorption degree as the y-axis are as follows:

[0069] Preprocess the adsorption data to remove abnormal values ​​and obtain sample data;

[0070] A plane rectangular coordinate system is established with time as the x-axis and adsorption degree as the y-axis;

[0071] Fill the sample data into the rectangular coordinate system in the form of scattered points;

[0072] A standard kinetic equation is preset, and the sample data is substituted into the equation for matching and screening. The equation with the smallest difference value is the target kinetic equation;

[0073] According to natural factors and environmental factors, the fluctuation factors are configured for the target kinetic equation to obtain the mineral adsorption curve equation;

[0074] The standard kinetic equation cannot describe the mineral adsorption curve very well at any stage, so natural factors and environmental factors are configured as fluctuation factors. It is more preferable to establish a piecewise equation or obtain the mineral adsorption curve equation through network learning.

[0075] The mineral adsorption curve is established according to the mineral adsorption curve equation.

[0076] S4. Obtain a predicted immobilization value according to the immobilization prediction model, and compare the predicted immobilization value with the current heavy metal distribution to determine the types of clay minerals and their distribution in the passivated excessive heavy metals;

[0077] Preferably, the retention prediction value is obtained according to the retention prediction model, and the retention prediction value is compared with the current heavy metal distribution to determine the types of clay minerals in the passivated excessive heavy metals and their distribution.

[0078] The clay mineral with the first area order is selected as the target mineral;

[0079] The rendering blocks with non-zero predicted retention values ​​are selected as target mineral distribution blocks. A predicted retention value of zero indicates that the current soil environment can repair itself.

[0080] S5. Determine the effective content of heavy metals based on the risk index, and generate a strategy for controlling complex heavy metal pollution in the soil of non-ferrous smelting sites based on the distribution of heavy metals.

[0081] Preferably, the effective content of heavy metals is determined according to the risk index, and the specific content of the strategy for treating the composite heavy metal pollution in the soil of non-ferrous smelting sites is generated in combination with the distribution of heavy metals:

[0082] A risk index is preset according to different social and environmental conditions, and the current effective content of target heavy metals in the soil is calculated based on the risk index;

[0083] Get the current heavy metal content based on the heavy metal distribution;

[0084] The predicted immobilization values ​​were analyzed to obtain the predicted adsorbed heavy metal content;

[0085] Calculate the target adsorbed heavy metal content as the difference between the current heavy metal content and the predicted adsorbed heavy metal content and the target heavy metal effective state content;

[0086] The total target mineral demand is obtained based on the target heavy metal adsorption content and the immobilization prediction model;

[0087] A genetic algorithm is used to calculate the target mineral block demand in the target mineral distribution block to generate a strategy for controlling complex heavy metal pollution in the soil of non-ferrous smelting sites.

[0088] Risk indices include health risk index (HQ) and cancer risk index (CR). HQ is calculated by comparing the dose of human exposure to a pollutant with the safety threshold of the pollutant.

[0089] In the field of mineral processing, HQ can be used to assess the risk level of pollutants to human health and formulate corresponding risk management measures accordingly.

[0090] As part of the natural environment, the distribution and content of minerals may be related to factors such as the geological background, soil type, and climate conditions of the region. These factors may indirectly affect the local ecological environment and the lifestyle of residents, thus generating a certain correlation with CR. For example, some regions may have a lower incidence of cancer because they are rich in certain beneficial minerals.

[0091] Preferably, the specific content of the strategy for treating the soil composite heavy metal pollution in non-ferrous smelting sites by using the genetic algorithm to calculate the target mineral block demand of the target mineral distribution block is as follows:

[0092] Determine the accuracy of the current genetic algorithm according to environmental requirements, and divide the total demand for target minerals into demand monomers according to the accuracy of the genetic algorithm;

[0093] Match the demand monomers into the target mineral blocks and make retention predictions in the retention prediction model;

[0094] Calculate the predicted area of ​​different blocks until the predicted area of ​​different blocks is 0 and stop the iteration. Record the current number of monomers required in different blocks, and generate the target mineral block demand based on the current number of monomers required in different blocks to generate a strategy for controlling complex heavy metal pollution in the soil of non-ferrous smelting sites.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical method of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical method to deviate from the spirit and scope of the technical method of the present invention.

Claims

1. A method for treating soil composite heavy metal pollution in non-ferrous smelting sites, characterized in that: The following steps are involved: S1. Obtain the soil conditions of the site, analyze the soil conditions of the site to obtain the soil layout, clay mineral conditions and heavy metal distribution of the site; S2. Conduct heavy metal adsorption experiments on clay minerals to obtain adsorption data; S3, construct a retention prediction model based on site soil layout, clay minerals, heavy metal distribution and adsorption data; S4. Obtain a predicted immobilization value according to the immobilization prediction model, and compare the predicted immobilization value with the current heavy metal distribution to determine the types of clay minerals and their distribution in the passivated excessive heavy metals; S5. Determine the effective content of heavy metals based on the risk index and generate a strategy for treating soil composite heavy metal pollution in non-ferrous smelting sites based on the distribution of heavy metals; The specific contents of building a fixation prediction model based on site soil layout, heavy metal distribution and adsorption data include: Generate a virtual model of the site based on the site soil layout ratio, and divide the virtual model into blocks according to the distribution of heavy metals to obtain heavy metal blocks; Analyze the clay minerals to obtain the types and distribution of clay minerals; Generate corresponding identifiers according to the types of clay minerals, and mark and display the identifiers in the heavy metal blocks according to the distribution of clay minerals; The heavy metal range is rendered with the identifier as the center to obtain a rendering block, and the depth of the rendering color is determined according to the heavy metal concentration; Calculate the area of ​​the rendering blocks and sort them from small to large to get the area order; The specific contents of building a fixation prediction model based on site soil layout, heavy metal distribution and adsorption data also include: Acquire adsorption data, establish a mineral adsorption curve with time as the x-axis and adsorption degree as the y-axis, and establish a display window on the adsorption curve; The identifier and its rendering area are intercepted, and the corresponding mineral adsorption curve is matched according to the identifier, and the corresponding display window is matched according to the heavy metal concentration; According to the corresponding situation of the y-axis of the adsorption curve, rendering prediction is performed in the remaining display window of the corresponding mineral adsorption curve; The predicted display windows are merged in chronological order to generate a dynamic retention prediction model; The predicted immobilization value is obtained according to the immobilization prediction model. The predicted immobilization value is compared with the current heavy metal distribution to determine the types of clay minerals in the passivated excessive heavy metals and their distribution. The specific contents are as follows: The clay mineral with the first area order is selected as the target mineral; Filter the rendering blocks with non-zero retention prediction values ​​as target mineral distribution blocks; The effective content of heavy metals is determined based on the risk index, and the specific content of the strategy for treating the soil composite heavy metal pollution in non-ferrous smelting sites is generated based on the distribution of heavy metals: A risk index is preset according to different social and environmental conditions, and the current effective content of target heavy metals in the soil is calculated based on the risk index; Get the current heavy metal content based on the heavy metal distribution; The predicted immobilization values ​​were analyzed to obtain the predicted adsorbed heavy metal content; Calculate the target adsorbed heavy metal content as the difference between the current heavy metal content and the predicted adsorbed heavy metal content and the target heavy metal effective state content; The total target mineral demand is obtained based on the target heavy metal adsorption content and the immobilization prediction model; A genetic algorithm is used to calculate the target mineral block demand in the target mineral distribution block to generate a strategy for controlling complex heavy metal pollution in the soil of non-ferrous smelting sites.

2. The method for treating soil composite heavy metal pollution in non-ferrous smelting sites according to claim 1, characterized in that: The specific contents of obtaining adsorption data and establishing a mineral adsorption curve with time as the x-axis and adsorption degree as the y-axis are as follows: Preprocess the adsorption data to remove abnormal values ​​and obtain sample data; A plane rectangular coordinate system is established with time as the x-axis and the adsorption degree as the y-axis; Fill the sample data into the rectangular coordinate system in the form of scattered points; A standard kinetic equation is preset, and the sample data is substituted into the equation for matching and screening. The equation with the smallest difference value is the target kinetic equation; According to natural factors and environmental factors, the fluctuation factors are configured for the target kinetic equation to obtain the mineral adsorption curve equation; The mineral adsorption curve is established according to the mineral adsorption curve equation.

3. The method for treating soil composite heavy metal pollution in non-ferrous smelting sites according to claim 1, characterized in that: The specific contents of the strategy for treating the soil composite heavy metal pollution in non-ferrous smelting sites by using genetic algorithm to calculate the target mineral block demand of the target mineral distribution block are as follows: Determine the accuracy of the current genetic algorithm according to environmental requirements, and divide the total demand for target minerals into demand monomers according to the accuracy of the genetic algorithm; Match the demand monomers into the target mineral blocks and make retention predictions in the retention prediction model; Calculate the predicted area of ​​different blocks until the predicted area of ​​different blocks is 0 and stop the iteration. Record the current number of monomers required in different blocks, and generate the target mineral block demand based on the current number of monomers required in different blocks to generate a strategy for controlling complex heavy metal pollution in the soil of non-ferrous smelting sites.

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